In this paper, we propose a novel scene text script identification method which uses spatial attention based sequence learning approach for script identification in scene text images. We use spatial attention blocks which extract rich text features while ignoring the disruptions present in the scene text. Inspired by the successful application of residual connections in image classification tasks [1]}, [2]}, we employ hierarchical residual connections to build a deeper network that helps in learning representative image features. In training phase, instead of learning a single representation of the text and failing to capture the discriminating chars of each script, a segmentation free, sequence to sequence mapping is done using Connectionist Temporal Classification (CTC) to supervise the predictions. The key contributions of our scene text script identification method are:
